{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据概述\n",
    "[代码参考](https://www.kaggle.com/dmitriy19/d/kaggle/us-baby-names/exploring-us-baby-names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 引入相关数据分析包\n",
    "* pandas，用于数据集读取，处理和分析\n",
    "* numpy，用于数据集处理\n",
    "* matplotlib，用于数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mary</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Anna</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Emma</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Minnie</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id       Name  Year Gender  Count\n",
       "0   1       Mary  1880      F   7065\n",
       "1   2       Anna  1880      F   2604\n",
       "2   3       Emma  1880      F   2003\n",
       "3   4  Elizabeth  1880      F   1939\n",
       "4   5     Minnie  1880      F   1746"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./dataset/NationalNames.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据详情"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1825433 entries, 0 to 1825432\n",
      "Data columns (total 5 columns):\n",
      "Id        int64\n",
      "Name      object\n",
      "Year      int64\n",
      "Gender    object\n",
      "Count     int64\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 69.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 全美流行的婴儿名字 top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将“姓名”和其数量进行统计，并放入“字典”变量中\n",
    "names_dict = dict()\n",
    "\n",
    "# 由于数据量多，耗时较长\n",
    "for index, row in data.iterrows():\n",
    "    # 按行遍历数据\n",
    "    if row['Name'] not in names_dict:\n",
    "        names_dict[row['Name']] = row['Count']\n",
    "    else:\n",
    "        names_dict[row['Name']] += row['Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary -> 4130441\n",
      "Minnie -> 159352\n"
     ]
    }
   ],
   "source": [
    "name = 'Mary'\n",
    "print '%s -> %i' %(name, names_dict.get(name))\n",
    "\n",
    "name = 'Minnie'\n",
    "print '%s -> %i' %(name, names_dict.get(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全美流行的婴儿名字top10：\n",
      "姓名：James -> 数量：5129096\n",
      "姓名：John -> 数量：5106590\n",
      "姓名：Robert -> 数量：4816785\n",
      "姓名：Michael -> 数量：4330805\n",
      "姓名：Mary -> 数量：4130441\n",
      "姓名：William -> 数量：4071368\n",
      "姓名：David -> 数量：3590557\n",
      "姓名：Joseph -> 数量：2580687\n",
      "姓名：Richard -> 数量：2564867\n",
      "姓名：Charles -> 数量：2376700\n"
     ]
    }
   ],
   "source": [
    "# 使用Counter计数器的most_common函数进行统计。\n",
    "# 返回最常用的元素及其计数的列表\n",
    "top_10 = Counter(names_dict).most_common(10)\n",
    "print '全美流行的婴儿名字top10：'\n",
    "for pair in top_10:\n",
    "    print '姓名：%s -> 数量：%i' %(pair[0], pair[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 全美最不流行婴儿名字 top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全美最不流行婴儿名字top10：\n",
      "姓名：Jaede -> 数量：5\n",
      "姓名：Wavalene -> 数量：5\n",
      "姓名：Tyieshia -> 数量：5\n",
      "姓名：Lillya -> 数量：5\n",
      "姓名：Pauleta -> 数量：5\n",
      "姓名：Deavyn -> 数量：5\n",
      "姓名：Taonna -> 数量：5\n",
      "姓名：Elijahray -> 数量：5\n",
      "姓名：Graysie -> 数量：5\n"
     ]
    }
   ],
   "source": [
    "print('全美最不流行婴儿名字top10：')\n",
    "# most_common()返回list，使用切片操作选取最后10个数据\n",
    "for pair in Counter(names_dict).most_common()[:-10:-1]:\n",
    "    print '姓名：%s -> 数量：%i' %(pair[0], pair[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 名字的平均长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def average_length_data_transform():\n",
    "    \"\"\"\n",
    "        统计每年男性、女性姓名的平均长度\n",
    "    \"\"\"\n",
    "    # 按行遍历数据\n",
    "    years = []\n",
    "    \n",
    "    # 女性姓名\n",
    "    female_average_length = []\n",
    "    female_average_name_length = dict()\n",
    "    \n",
    "    # 男性姓名\n",
    "    male_average_length = []\n",
    "    male_average_name_length = dict()\n",
    "    \n",
    "    for index, row in data.iterrows():\n",
    "        # 按行遍历数据集\n",
    "        if row['Gender'] == 'F':\n",
    "            # 女性\n",
    "            \n",
    "            # 获取当前年份\n",
    "            curr_year = row['Year']            \n",
    "            # 当前记录姓名长度\n",
    "            curr_name_length = len(row['Name'])\n",
    "            \n",
    "            if curr_year not in female_average_name_length:\n",
    "                # 如果当前年份不在“字典”中，放入字典中\n",
    "                # 比如: 1880年第一条女性记录：1 Mary 1880 F 7065，将做如下处理\n",
    "                # {1880: [4, 1]}\n",
    "                female_average_name_length[curr_year] = [curr_name_length, 1]\n",
    "            else:\n",
    "                # 比如: 1880年第二条女性记录: 2 Anna 1880 F 2604，将做如下处理\n",
    "                # {1880: [4+4, 1+1]} -> {1880: [8, 2]}\n",
    "                female_average_name_length[curr_year][0] += curr_name_length\n",
    "                female_average_name_length[curr_year][1] += 1\n",
    "        else:\n",
    "            # 男性\n",
    "            \n",
    "            # 处理过程同上\n",
    "            curr_year = row['Year']\n",
    "            curr_name_length = len(row['Name'])\n",
    "            if curr_year not in male_average_name_length:\n",
    "                male_average_name_length[curr_year] = [curr_name_length, 1]\n",
    "            else:\n",
    "                male_average_name_length[curr_year][0] += curr_name_length\n",
    "                male_average_name_length[curr_year][1] += 1\n",
    "    \n",
    "    # 遍历处理后的字典\n",
    "    # 女性\n",
    "    for key, value in female_average_name_length.items():\n",
    "        years.append(key)\n",
    "        female_average_length.append(float(value[0]) / value[1])\n",
    "    \n",
    "    # 男性   \n",
    "    for key, value in male_average_name_length.items():\n",
    "        years.append(key)\n",
    "        male_average_length.append(float(value[0]) / value[1])\n",
    "        \n",
    "    return (female_average_length, female_average_name_length, male_average_length, male_average_name_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 处理过程较长\n",
    "female_average_length, female_average_name_length, male_average_length, male_average_name_length = average_length_data_transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "年份:1880，总长：5439，个数：942\n",
      "年份:1881，总长：5394，个数：938\n",
      "年份:1882，总长：5968，个数：1028\n",
      "年份:1883，总长：6083，个数：1054\n",
      "年份:1884，总长：6792，个数：1172\n",
      "年份:1885，总长：6910，个数：1197\n",
      "年份:1886，总长：7422，个数：1282\n",
      "年份:1887，总长：7529，个数：1306\n",
      "年份:1888，总长：8486，个数：1474\n",
      "年份:1889，总长：8494，个数：1479\n",
      "年份:1890，总长：8856，个数：1534\n"
     ]
    }
   ],
   "source": [
    "# 查看结果 \n",
    "for year in range(1880, 1891):\n",
    "    print '年份:%i，总长：%i，个数：%i' %(year, female_average_name_length.get(year)[0], female_average_name_length.get(year)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.773885350318471, 5.750533049040512, 5.80544747081712, 5.77134724857685, 5.795221843003413, 5.772765246449457, 5.789391575663027, 5.764931087289433, 5.757123473541384, 5.743069641649764]\n"
     ]
    }
   ],
   "source": [
    "print female_average_length[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GZ56B0aPTd9+5c8O8edCkiU3IhgxJ3/2rO54mS2l0+vRpLl68yJIlSwgMDPR0OEopdVeJ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\nrnz9NdxwA1SsCBMnQr58QUckIhIyTcMTEZHo8R7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v9N5PjkRgIhIBe/fays2G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EmSJDUUb0qrxjVvHhxzTA5At91WvSpBBAwaBB07wjnnQKtWsNZasPHGpelvGRs/HubMMSxJkiQ1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\nSZKk0jAsS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EkqDy7DkyRJkqQiDEuSJEmSVIRhSZIkSZKKMCxJkiRJUhGGJUmSJEkqwrAk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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7b31a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# years = [1880, 1881, ..., 2014]\n",
    "years = range(1880, 2015)\n",
    "\n",
    "# 使用matplotlib进行数据可视化\n",
    "f, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# 设置x轴数值范围\n",
    "ax.set_xlim([1880, 2014])\n",
    "\n",
    "# years为x轴，average_length为y轴\n",
    "# 女性曲线为红色，男性为蓝色\n",
    "plt.plot(years, female_average_length, label='Average length of female names', color='r')\n",
    "plt.plot(years, male_average_length, label='Average length of male names', color='b')\n",
    "\n",
    "# 设置x,y轴标签\n",
    "ax.set_ylabel('Length of name')\n",
    "ax.set_xlabel('Year')\n",
    "\n",
    "# 设置图像名称\n",
    "ax.set_title('Average length of names')\n",
    "\n",
    "# 设置图例\n",
    "legend = plt.legend(loc='best', frameon=True, borderpad=1, borderaxespad=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 唯一姓名分析\n",
    "课后作业，自己阅读代码并查阅相关API进行分析并理解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_in_each_year = dict()\n",
    "years = range(1880, 2015)\n",
    "\n",
    "for each_year in years:\n",
    "    each_year_data = data[data['Year'] == each_year]\n",
    "    top_in_each_year[each_year] = dict()\n",
    "    for index, row in each_year_data.iterrows():            \n",
    "        top_in_each_year[each_year][row['Name']] = row['Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_sum = []\n",
    "top_25_sum = []\n",
    "for year, names_in_year in top_in_each_year.items():\n",
    "    all_sum.append(sum(Counter(names_in_year).values()))\n",
    "    top_25 = Counter(names_in_year).most_common(25)\n",
    "    sum_temp = 0\n",
    "    for pair in top_25:\n",
    "        sum_temp += pair[1]\n",
    "    top_25_sum.append(sum_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "percent_unique_names = np.array(top_25_sum).astype(float) / np.array(all_sum) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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usTu0ezidzsc+9vr1azgcDl55pTjp02eIxqisZDMgIC0OhyNay5WYo54kERERERtcvXqF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\nSZIkSVLK/wewCXFDCDAF0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e169710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_xlim([1880, 2014])\n",
    "\n",
    "plt.plot(years, percent_unique_names, label='Percent of unique names', color='black')\n",
    "\n",
    "ax.set_ylabel('Percent of unique names')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_title('Percent of unique names')\n",
    "legend = plt.legend(loc='best', frameon=True, borderpad=1, borderaxespad=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
